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Evaluating the Yield of Hybrid Napier Grass with Data Mining Techniques

Author(s): Nadiammai G. V. | Krishnaveni S. | M. Hemalatha

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: 35;
Issue: 5;
Start page: 1;
Date: 2011;
Original page

Keywords: Data mining | Machine learning | Naive Bayes classifiers | J48 Rule Induction | Single Rule Induction | Decision Stump | ID3 | Random Forest

Data Mining is the process of identifying the hidden patterns from large amount of data. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning techniques are mainly focused as research through which we learnt to recognize complex and make intelligent decisions based on data. This paper involves the information about the yield of the hybrid grass from NBH1 to NBH11. The hybrid grass enhances the milk production in the states of Tamilnadu, Kerala, Karnataka, Andhra Pradesh, Orissa, and Maharashtra and Gujarat. It is well adapted to the soil and climatic conditions of Tamilnadu. In this paper, some of classification models are used to predict the yield of hybrid grass. They are NaiveBayes, J48, Rule Induction, Single Rule Induction, Decision Stump, ID3 and Random Forest.
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